Compression schemes are generally of two kinds, lossy and lossless. Lossy compression compresses an original signal by removing some information from being encoded in the compressed signal, such that the signal upon decoding is no longer identical to the original signal. For example, many modern lossy audio compression schemes use human auditory models to remove signal components that are perceptually undetectable or almost undetectable by human ears. Such lossy compression can achieve very high compression ratios, making lossy compression well suited for applications, such as internet music streaming, downloading, and music playing in portable devices.
On the other hand, lossless compression compresses a signal without loss of information. After decoding, the resulting signal is identical to the original signal. Compared to lossy compression, lossless compression achieves a very limited compression ratio. A 2:1 compression ratio for lossless audio compression usually is considered good. Lossless compression thus is more suitable for applications where perfect reconstruction is required or quality is preferred over size, such as music archiving and DVD audio.
Traditionally, an audio compression scheme is either lossy or lossless. However, there are applications where neither compression type is best suited. For example, practically all modern lossy audio compression schemes use a frequency domain method and a psychoacoustic model for noise allocation. Although the psychoacoustic model works well for most signals and most people, it is not perfect. First, some users may wish to have the ability to choose higher quality levels during portions of an audio track where degradation due to lossy compression is most perceptible. This is especially important when there is no good psychoacoustic model that can appeal to their ears. Secondly, some portions of the audio data may defy any good psychoacoustic model, so that the lossy compression uses a lot of bits—even data “expansion” in order to achieve the desired quality. In this case, lossless coding may be more efficient.